Solving robot path planning problem by using a new elitist multi-objective IWD algorithm based on coefficient of variation

In this paper, we want to solve multi-objective robot path planning problem. A new elitist multi-objective approach is proposed to determine Pareto front based on coefficient of variation. Intelligent water drops (IWD) algorithm is generalized by this approach and as a new multi-objective IWD algorithm. We call our new algorithm CV-based MO-IWD. It tried to optimize two objectives: length and safety of the path. In the CV-based MO-IWD, we want to discover solutions as close to optimal Pareto solutions as possible and find solutions as diverse as possible in the obtained Pareto front. In this way, coefficient of variation of Pareto front is determined in each objective. Then, appropriate number of heuristic operations (local search in this paper) is calculated and applied for each solution. Implementation results and comparisons with NSGA_II algorithm show the ability of the proposed approach to achieve a near optimal Pareto front with a good diversity, while the number of fitness function calls does not increase. This method is superior because of suitable distribution of heuristic operations.

[1]  Daniel Angus,et al.  Multiple objective ant colony optimisation , 2009, Swarm Intelligence.

[2]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[3]  Hamed Shah-Hosseini,et al.  The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm , 2009, Int. J. Bio Inspired Comput..

[4]  A. Osyczka,et al.  A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm , 1995 .

[5]  Corina Cimpanu,et al.  Multiobjective hybrid evolutionary path planning with adaptive pareto ranking of variable-length chromosomes , 2014, 2014 IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI).

[6]  Ellips Masehian,et al.  Classic and Heuristic Approaches in Robot Motion Planning A Chronological Review , 2007 .

[7]  Christine Solnon,et al.  Ant Colony Optimization for Multi-Objective Optimization Problems , 2007 .

[8]  O. Castilho,et al.  Multiple Objective Optimization Genetic Algorithms For Path Planning In Autonomous Mobile Robots , 2005, Int. J. Comput. Syst. Signals.

[9]  Homayun Motameni,et al.  Optimal path planning for mobile robot using Intelligent Water Drops algorithm , 2014, J. Intell. Fuzzy Syst..

[10]  Sandeep Kumar,et al.  A New Ecologically Inspired Algorithm for Mobile Robot Navigation , 2014, FICTA.

[11]  Kalyanmoy Deb,et al.  Multi-objective path planning using spline representation , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.

[12]  Naoyuki Kubota,et al.  Bacterial memetic algorithm for offline path planning of mobile robots , 2012, Memetic Comput..

[13]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[14]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[15]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[16]  Lothar Thiele,et al.  An evolutionary algorithm for multiobjective optimization: the strength Pareto approach , 1998 .

[17]  Anthony Stentz,et al.  The Focussed D* Algorithm for Real-Time Replanning , 1995, IJCAI.

[18]  Kalyanmoy Deb,et al.  Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms , 2012, Soft Computing.

[19]  Dun-Wei Gong,et al.  Robot path planning in uncertain environments based on particle swarm optimization , 2009, 2009 IEEE Congress on Evolutionary Computation.

[20]  Wang Zhangqi,et al.  Mobile Robot Path Planning based on Parameter Optimization Ant Colony Algorithm , 2011 .

[21]  R. Hartl,et al.  Are COMPETants more competent for problem solving? The case of a multiple objective transportation problem. , 2001 .

[22]  Jianhua Zhang,et al.  Multi-objective Particle Swarm Optimization for Robot Path Planning in Environment with Danger Sources , 2011, J. Comput..

[23]  Ivan Petrovic,et al.  Two-way D* algorithm for path planning and replanning , 2011, Robotics Auton. Syst..

[24]  Adem Tuncer,et al.  Dynamic path planning of mobile robots with improved genetic algorithm , 2012, Comput. Electr. Eng..

[25]  Benjamín Barán,et al.  A Multiobjective Ant Colony System for Vehicle Routing Problem with Time Windows , 2003, Applied Informatics.

[26]  L Manuel,et al.  The Automatic Design of Multi-Objective Ant Colony Optimization Algorithms , 2012 .

[27]  H. Shah-Hosseini.  An approach to continuous optimization by the Intelligent Water Drops algorithm , 2012 .

[28]  Panagiotis Tsiotras,et al.  Incremental Multi-Scale Search Algorithm for Dynamic Path Planning With Low Worst-Case Complexity , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[29]  Panagiotis Tsiotras,et al.  Beamlet-like data processing for accelerated path-planning using multiscale information of the environment , 2010, 49th IEEE Conference on Decision and Control (CDC).

[30]  Richard F. Hartl,et al.  Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection , 2004, Ann. Oper. Res..

[31]  Mansoor Davoodi Monfared,et al.  Multi-objective path planning in discrete space , 2013, Appl. Soft Comput..